The effect of head position on the distribution of topical nasal medication using the Mucosal Atomization Device: a cadaver study
Bibliographic record
Abstract
BACKGROUND: The Mucosal Atomization Device (MAD) distributes medication throughout the paranasal sinuses for patients with chronic rhinosinusitis (CRS). Determining the optimal head position is important to ensure maximal delivery of medication to the sinus cavities. The objective of this work was to determine the effect of the lying-head-back (LHB) and head-down and forward (HDF) position, on the distribution of topical nasal medication via MAD in cadaver specimens. METHODS: Twenty specimens having received complete functional endoscopic sinus dissection were chosen. The MAD was used to administer 2 mL of fluorescein-impregnated saline solution through the nose in both the LHB and HDF positions. Fluorescein was identified on 11 predetermined anatomical areas using a blue light filter. Three blinded investigators assessed endoscopic images to determine the presence of fluorescein. RESULTS: A total of 440 anatomical locations (n = 20 cadavers) received administration of the fluorescein nasal spray in the LHB or HDF position. LHB position had significantly greater total distribution to all pertinent anatomical sites than the HDF position (76% vs 41%; p < 0.001; 95% confidence interval [CI], 0.26-0.44). The proportion of staining was significantly greater for the ethmoid (p = 0.11; 95% CI, 0.05-0.66), frontal (p < 0.01; 95% CI, 0.20-0.80), and sphenoid sinuses (p = 0.03; 95% CI, 0.07-0.73) when compared to the HDF position. CONCLUSION: A greater distribution of medication to the sinonasal cavities was observed in the LHB position compared to the HDF position. These areas are of particular clinical relevance in postsurgical patients with refractory CRS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".